Two-Stage Gamma-Neutron Source Classification in Water Cherenkov Detectors: Energy Threshold Screening and Machine Learning Pulse Analysis

Abstract

Water Cherenkov detectors offer a robust and economical solution for real-time radiation monitoring by detecting Cherenkov light from charged particles moving faster than light in water. This work presents a novel two-stage classification framework for gamma-neutron discrimination: an initial physics-based energy threshold filters unambiguous low-energy gamma sources, followed by a machine learning ensemble that resolves ambiguities at higher energies. The detector response was characterized using $^{60}$Co (1.17/1.33~MeV), $^{137}$Cs (0.66~MeV), and a shielded $^{241}$AmBe source, with lead, paraffin, and cadmium shielding employed to isolate neutron and gamma interactions. Energy calibration established a linear ADU to MeV conversion ($R^2 = 0.966$), enabling identification of a neutron detection threshold at $2.62 \pm 0.77$~MeV via a $3\sigma$ significance analysis. Stage one categorizes sources as pure gamma (below threshold) or neutron-emitting (at threshold). For ambiguous cases above threshold, a machine learning pipeline utilizing pulse shape analysis was developed. A soft voting ensemble (Bagging, CatBoost, and MLP) achieved an accuracy of 0.816 and an AUC of 0.921. This hybrid scheme combines physics-based filtering with ML refinement, offering an interpretable and scalable solution for nuclear security, nonproliferation monitoring, and fundamental radiation research.